Blood flow (BF) maps of a tissue or organ of a subject calculated from magnetic resonance imaging (MRI), especially dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) are widely used to provide useful information about the conditions of the tissue or organ, especially quantification of tissue or organ perfusion. For example, cerebral blood flow (CBF) maps can provide information assisting a medical practitioner in diagnosis of acute stroke and cancer. The determination of accurate BF maps such as CBF maps requires accurate detection of arterial input function (AIF). However, the determination of accurate BF maps such as CBF maps cannot be achieved in real-time. One of the limiting factors is the difficulty in automatically determining the arterial input function (AIF), which is estimated from the signal change in a major artery. Small variation of the AIF may have a huge effect on the resultant BF, mean transit time (MTT), and other crucial parameters [3]. The current manual AIF determination procedures depend on operators' experience and subjective judgments, which is less reproducible. It is desirable that the AIF can be estimated accurately with no user interaction. To achieve this goal, various automatic and semiautomatic procedures have been proposed for AIF determination [3, 14, 15]. Based on the shape features of the concentration time curves, K-means cluster analysis (KCA) has been applied for automatic AIF selection [3]. The drawback of KCA is that the clustering result is unstable and sensitive to initialization.